Token Classification
Transformers
Safetensors
layoutlmv3
ner
on-device
privacy
flowx
openner
cross
de-identification
Instructions to use flowxai/docformner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/docformner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/docformner")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("flowxai/docformner") model = AutoModelForTokenClassification.from_pretrained("flowxai/docformner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "LayoutLMv3ForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "coordinate_size": 128, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "has_relative_attention_bias": true, | |
| "has_spatial_attention_bias": true, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-BORROWER", | |
| "2": "I-BORROWER", | |
| "3": "B-LENDER", | |
| "4": "I-LENDER", | |
| "5": "B-PROPERTY_ADDR", | |
| "6": "I-PROPERTY_ADDR", | |
| "7": "B-LOAN_AMOUNT", | |
| "8": "I-LOAN_AMOUNT", | |
| "9": "B-LTV", | |
| "10": "I-LTV", | |
| "11": "B-INTEREST_RATE", | |
| "12": "I-INTEREST_RATE", | |
| "13": "B-TERM_YEARS", | |
| "14": "I-TERM_YEARS", | |
| "15": "B-APP_NO", | |
| "16": "I-APP_NO", | |
| "17": "B-INCOME", | |
| "18": "I-INCOME" | |
| }, | |
| "initializer_range": 0.02, | |
| "input_size": 224, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-APP_NO": 15, | |
| "B-BORROWER": 1, | |
| "B-INCOME": 17, | |
| "B-INTEREST_RATE": 11, | |
| "B-LENDER": 3, | |
| "B-LOAN_AMOUNT": 7, | |
| "B-LTV": 9, | |
| "B-PROPERTY_ADDR": 5, | |
| "B-TERM_YEARS": 13, | |
| "I-APP_NO": 16, | |
| "I-BORROWER": 2, | |
| "I-INCOME": 18, | |
| "I-INTEREST_RATE": 12, | |
| "I-LENDER": 4, | |
| "I-LOAN_AMOUNT": 8, | |
| "I-LTV": 10, | |
| "I-PROPERTY_ADDR": 6, | |
| "I-TERM_YEARS": 14, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_2d_position_embeddings": 1024, | |
| "max_position_embeddings": 514, | |
| "max_rel_2d_pos": 256, | |
| "max_rel_pos": 128, | |
| "model_type": "layoutlmv3", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "patch_size": 16, | |
| "rel_2d_pos_bins": 64, | |
| "rel_pos_bins": 32, | |
| "second_input_size": 112, | |
| "shape_size": 128, | |
| "text_embed": true, | |
| "transformers_version": "5.14.1", | |
| "type_vocab_size": 1, | |
| "use_cache": false, | |
| "visual_embed": true, | |
| "vocab_size": 50265 | |
| } | |